Recent Advanced Driver Assistance Systems (ADAS) have vastly adopted artificial intelligence to reduce the rate of traffic accident, with many companies in the industry focusing their research and development on technologies, such as a lane line detection system, a parking assist system, a collision avoidance system and so forth.
Image recognition is one of the most important techniques in the aforementioned systems. The image recognition technique mostly focuses on training parameters of a classifier by virtue of an algorithm associated with machine learning for classification so as to recognize objects in an image. However, due to the restricted performance of an embedded system provided on a vehicle, performance of a classifier of the embedded system is limited. Therefore, how to effectively reduce the false positive rate in a diversified road environment while taking into consideration the restricted performance of a vehicle embedded system is a key point in current research and development projects.